The objective of ISSS was to describe architectures and develop software algorithms and hardware processing elements to improve data reduction and data fusion in real-time multi-sensor systems with applications in the demanding task of monitoring critically ill patients.
The aim was to develop an analysis, diagnosis, prediction and treatment Patient Monitoring System (PMS) which would continuously adjust and update its different knowledge bases, using supervised and non-real time learning on some of the knowledge bases and unsupervised real-time learning on others.
This advanced IT system was to be capable of supporting the complex, dynamic and distributed decision-making required in monitoring all critical stages of hospital treatment, where demand varies from frequent, complex data sampling from a large number of the same kind of sensor, to heavy sampling of data from very different groups of sensors where the imaging of signals varied significantly.
3015 GD Rotterdam
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